Solving Large-Scale Battery Distribution Problem in Charging Station Network Using Genetic Algorithm

机译：用遗传算法解决充电站网络的大规模电池分布问题

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How to accomplish large-scale battery optimal distribution is a very important and difficult problem in charging station network. Using traditional methods, such as Simulated Annealing, may not get satisfactory solution. Genetic Algorithm (GA), as a bionic-inspired optimization algorithm, has great potentials to solve combination optimization problems and is adopted to solve largescale battery optimal distribution problem. The steps for large-scale battery optimal distribution using GA are given out. The comparison experiment results show its validity to solve large-scale battery optimal distribution problem.

机译：如何完成大规模的电池最佳分布是充电站网络中的一个非常重要和难题。使用传统方法，例如模拟退火，可能无法获得令人满意的解决方案。遗传算法（GA），作为仿生启发优化算法，具有较大的潜力来解决组合优化问题，并采用求解大型电池最佳分布问题。使用GA的大规模电池最佳分布的步骤被给出。比较实验结果显示了解决大规模电池最佳分布问题的有效性。

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《International conference on information technology and industrial engineering》|2010年||共4页
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作者
Qingsheng Shi; Yanbo Hui; Xinmin Xiong;
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正文语种
中图分类信息与知识传播;
关键词
Battery distribution; charging station network; Simulated Annealing; Genetic Algorithm;

机译：电池分配;充电站网络;模拟退火;遗传算法;

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Solving Large-Scale Battery Distribution Problem in Charging Station Network Using Genetic Algorithm

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